Vital Energy
Based in Tulsa, OK, Vital Energy stands as a paragon of innovation within the upstream oil and gas sector. As a nexus between state-of-the-art technology and energy resource management, we have transformed the paradigm of operational efficacy through our innovative approaches. Not just a name, but a movement within the industry, Vital Energy epitomizes cutting-edge innovation, setting standards, and leading the charge with solutions that chart the future.
Role Description: Data Scientist I
As an entry-level Data Scientist I at our oil and gas company, you will play a crucial role in supporting our data-driven decision-making processes. Your main responsibilities will include:
•
Collecting, cleaning, and preprocessing data from various sources : This includes sensors, logs, and databases. •
Building and implementing machine learning models : This involves predicting and optimizing key performance indicators related to oil and gas operations. These indicators include production efficiency, equipment maintenance, and resource allocation. •
Creating clear and insightful data visualizations and dashboards : This involves communicating findings and insights to non-technical stakeholders. •
Collaborating with cross-functional teams : This includes engineers, geologists, and business analysts, to identify opportunities for data-driven improvements in operational processes. •
Staying up-to-date with industry trends and advancements in data science : This includes leveraging this knowledge to drive innovation within the organization. •
Maintaining thorough documentation : This includes data sources, methodologies, and model development to ensure transparency and reproducibility. •
Actively seeking opportunities : This involves enhancing the efficiency and effectiveness of data analysis processes. At Vital Energy, you will be more than just a Data Scientist. You’ll be at the heart of a team shaping the future of the oil and gas industry. Step into our world in Tulsa, and together, let’s revolutionize the energy landscape. Technical Qualifications •
Education:
Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. •
Programming Skills:
Proficiency in Python or R for data analysis and machine learning. •
Machine Learning:
Basic understanding of machine learning algorithms and statistical analysis techniques. •
Data Manipulation:
Proficiency in data manipulation libraries such as pandas and NumPy. •
Problem-Solving:
Strong problem-solving skills with attention to detail. •
Communication:
Excellent communication skills to convey complex technical concepts to non-technical stakeholders. •
Team Collaboration:
Ability to work collaboratively in a team-oriented environment. •
Adaptability:
Eagerness to learn and adapt to new challenges in a rapidly evolving field. •
Big Data:
Familiarity with big data technologies, especially within the AWS ecosystem. •
Data Extraction:
Experience with data extraction and transformation from unstructured sources. •
Cloud Computing:
Knowledge of cloud computing platforms, specifically AWS, including AWS Lambda, S3, Glue, and SageMaker. •
Geospatial Data:
Understanding of geospatial data analysis techniques. •
Relevant Experience:
Previous internships or project work in data science or analytics. Making job hunting smarter, faster, and way more fun. Our AI-powered, mobile-first platform connects students and early-career pros with real opportunities that match their skills and goals. With gamified skill validation, personalized career paths, and smart matching, we’re not just helping you land a job—we’re setting you up for a career you’ll actually love.
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Collecting, cleaning, and preprocessing data from various sources : This includes sensors, logs, and databases. •
Building and implementing machine learning models : This involves predicting and optimizing key performance indicators related to oil and gas operations. These indicators include production efficiency, equipment maintenance, and resource allocation. •
Creating clear and insightful data visualizations and dashboards : This involves communicating findings and insights to non-technical stakeholders. •
Collaborating with cross-functional teams : This includes engineers, geologists, and business analysts, to identify opportunities for data-driven improvements in operational processes. •
Staying up-to-date with industry trends and advancements in data science : This includes leveraging this knowledge to drive innovation within the organization. •
Maintaining thorough documentation : This includes data sources, methodologies, and model development to ensure transparency and reproducibility. •
Actively seeking opportunities : This involves enhancing the efficiency and effectiveness of data analysis processes. At Vital Energy, you will be more than just a Data Scientist. You’ll be at the heart of a team shaping the future of the oil and gas industry. Step into our world in Tulsa, and together, let’s revolutionize the energy landscape. Technical Qualifications •
Education:
Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. •
Programming Skills:
Proficiency in Python or R for data analysis and machine learning. •
Machine Learning:
Basic understanding of machine learning algorithms and statistical analysis techniques. •
Data Manipulation:
Proficiency in data manipulation libraries such as pandas and NumPy. •
Problem-Solving:
Strong problem-solving skills with attention to detail. •
Communication:
Excellent communication skills to convey complex technical concepts to non-technical stakeholders. •
Team Collaboration:
Ability to work collaboratively in a team-oriented environment. •
Adaptability:
Eagerness to learn and adapt to new challenges in a rapidly evolving field. •
Big Data:
Familiarity with big data technologies, especially within the AWS ecosystem. •
Data Extraction:
Experience with data extraction and transformation from unstructured sources. •
Cloud Computing:
Knowledge of cloud computing platforms, specifically AWS, including AWS Lambda, S3, Glue, and SageMaker. •
Geospatial Data:
Understanding of geospatial data analysis techniques. •
Relevant Experience:
Previous internships or project work in data science or analytics. Making job hunting smarter, faster, and way more fun. Our AI-powered, mobile-first platform connects students and early-career pros with real opportunities that match their skills and goals. With gamified skill validation, personalized career paths, and smart matching, we’re not just helping you land a job—we’re setting you up for a career you’ll actually love.
#J-18808-Ljbffr